import pickle
import gradio as gr
import re
import spacy
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from sklearn.linear_model import PassiveAggressiveClassifier

# Ensure required Spacy model is installed
import subprocess
import sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "spacy"])
subprocess.check_call([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])

# Load the saved vectorizer and model
with open('tfidf_vectorizer.pkl', 'rb') as vectorizer_file:
    tfidf_vectorizer = pickle.load(vectorizer_file)

with open('pac_model.pkl', 'rb') as model_file:  # Updated to PAC model
    pac_model = pickle.load(model_file)

# Load Spacy language model
nlp = spacy.load("en_core_web_sm")

class TextPreprocessing:
    @staticmethod
    def Cleaning_text(text: str) -> str:
        """
        Cleans the input text by converting to lowercase,
        removing URLs, special characters, and unnecessary spaces.
        """
        text = text.lower()
        text = re.sub(r'http\S+|www\S+|https\S+', '', text)
        text = re.sub(r"[^a-zA-Z\s]", '', text)
        text = re.sub(r"n't", ' not', text)
        text = re.sub(r"'s", '', text)
        text = re.sub(r'\s+', ' ', text).strip()
        return text


def preprocess_text(text):
    """
    Preprocess the text by cleaning it using the TextPreprocessing class.
    """
    cleaned_text = TextPreprocessing.Cleaning_text(text)
    return cleaned_text


def predict_news(text):
    """
    Predict whether the input news text is real or fake.
    """
    cleaned_text = preprocess_text(text)
    X_input = tfidf_vectorizer.transform([cleaned_text])
    prediction = pac_model.predict(X_input)
    return "Fake News" if prediction == 0 else "Real News"

# Gradio Interface
iface = gr.Interface(
    fn=predict_news, 
    inputs=gr.Textbox(lines=7, placeholder="Enter the news article here..."), 
    outputs="text", 
    title="Fake News Classification", 
    description="Classify news articles as real or fake using a Passive Aggressive Classifier."
)

iface.launch()